Portfolio Piece 01
Lilly McClendon
2025-02-07
Squirrel Eating Pizza!
The purpose of Portfolio Piece 1 is to create interactive maps using the mapview package. In fall of 2020, my friend sent me a picture of a squirrel eating a large piece of pizza up in a tree. As many people consider New York City the place for pizza, I am interested to see how the location of squirrel sightings in Central Park and pizza restaurants. Further, I want to see if squirrels were spotted more frequently eating near pizza restaurants.
Data Credits
Data from NYC Squirrel Census. Retrieved from jonthegeek originally shared by Sara Stoudt. Pizza Data
Load squirrel data and packages
library(tidyverse) nyc_squirrels <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2019/2019-10-29/nyc_squirrels.csv")## Rows: 3023 Columns: 36
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (14): unique_squirrel_id, hectare, shift, age, primary_fur_color, highli...
## dbl (9): long, lat, date, hectare_squirrel_number, zip_codes, community_dis...
## lgl (13): running, chasing, climbing, eating, foraging, kuks, quaas, moans, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Squirrel fur color visualization
ggplot(data=nyc_squirrels, aes(x=primary_fur_color, fill = primary_fur_color)) +
geom_bar(stat = "count", width = 1) +
scale_fill_manual(values = c("black", "chocolate4","darkgrey")) +
labs(title="NYC Squirrel Fur Color",
xlab = "Number of Observations",
ylab = "Fur Color")
This visualization utilizes the scale_fill_manual function in order to customize the colors of the bars to match the fur color they represent.
Load pizza data
pizza_datafiniti <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-01/pizza_datafiniti.csv")## Rows: 10000 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): name, address, city, country, province, categories
## dbl (4): latitude, longitude, price_range_min, price_range_max
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.pizza_nyc <- pizza_datafiniti %>%
filter(city == "New York")names(pizza_nyc)[names(pizza_nyc) == 'longitude'] <- 'long'
names(pizza_nyc)[names(pizza_nyc) == 'latitude'] <- 'lat'Combine squirrel and pizza data
nyc_squirrels <- nyc_squirrels %>%
mutate(source = "squirrels")
pizza_nyc <- pizza_nyc %>%
mutate(source = "pizza")
squirrels_and_pizza <- bind_rows(nyc_squirrels, pizza_nyc)
print(squirrels_and_pizza)## # A tibble: 3,678 × 45
## long lat unique_squirrel_id hectare shift date hectare_squirrel_number
## <dbl> <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 -74.0 40.8 37F-PM-1014-03 37F PM 10142018 3
## 2 -74.0 40.8 37E-PM-1006-03 37E PM 10062018 3
## 3 -74.0 40.8 2E-AM-1010-03 02E AM 10102018 3
## 4 -74.0 40.8 5D-PM-1018-05 05D PM 10182018 5
## 5 -74.0 40.8 39B-AM-1018-01 39B AM 10182018 1
## 6 -74.0 40.8 33H-AM-1019-02 33H AM 10192018 2
## 7 -74.0 40.8 6G-PM-1020-02 06G PM 10202018 2
## 8 -74.0 40.8 35C-PM-1013-03 35C PM 10132018 3
## 9 -74.0 40.8 7B-AM-1008-09 07B AM 10082018 9
## 10 -74.0 40.8 32E-PM-1017-14 32E PM 10172018 14
## # ℹ 3,668 more rows
## # ℹ 38 more variables: age <chr>, primary_fur_color <chr>,
## # highlight_fur_color <chr>,
## # combination_of_primary_and_highlight_color <chr>, color_notes <chr>,
## # location <chr>, above_ground_sighter_measurement <chr>,
## # specific_location <chr>, running <lgl>, chasing <lgl>, climbing <lgl>,
## # eating <lgl>, foraging <lgl>, other_activities <chr>, kuks <lgl>, …NYC Squirrel sightings and pizza restuarant plot.
ggplot(squirrels_and_pizza) +
geom_point(mapping = aes(x = long, y = lat,
color = source)) +
labs(
title= "New York City Pizza Restaurant Locations and Squirrel Sightings",
x = "Longitude",
y = "Latitude"
)
Install “sf” package in the R console by entering install.packages(“sf”). Install “terra” package by entering “install.packages(”terra) in the R console.
library(tidyverse)
library(sf)## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUElibrary(mapview)
mapview(squirrels_and_pizza, xcol = "long", ycol = "lat", crs = 4269, grid = FALSE, zcol = "source", legend = TRUE)